Point cloud completion addresses filling in the missing parts of a partial
point cloud obtained from depth sensors and generating a complete point cloud.
Although there has been steep progress in the supervised methods on the
synthetic point cloud completion task, it is hardly applicable in real-world
scenarios due to the domain gap between the synthetic and real-world datasets
or the requirement of prior information. To overcome these limitations, we
propose a novel self-supervised framework ACL-SPC for point cloud completion to
train and test on the same data. ACL-SPC takes a single partial input and
attempts to output the complete point cloud using an adaptive closed-loop (ACL)
system that enforces the output same for the variation of an input. We evaluate
our proposed ACL-SPC on various datasets to prove that it can successfully
learn to complete a partial point cloud as the first self-supervised scheme.
Results show that our method is comparable with unsupervised methods and
achieves superior performance on the real-world dataset compared to the
supervised methods trained on the synthetic dataset. Extensive experiments
justify the necessity of self-supervised learning and the effectiveness of our
proposed method for the real-world point cloud completion task. The code is
publicly available from this https URL

本研究提出了一种新的自监督框架 ACL-SPC 用于点云完整性恢复的培训和测试在相同的数据上。ACL-SPC 采用自适应闭环（ACL）系统，以在输入变化时强制输出相同的输出。实验结果表明，ACL-SPC 可以成功地学习完成部分点云并在真实世界数据集上比受过合成数据集训练的监督方法取得更好的性能，证明了自监督学习的必要性和所提出的方法的有效性。